Create README.md

README.md

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+---
+language: en
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- bert
+- emotion-classification
+- multi-label
+- goemotions
+- contrastive-learning
+- tri-tower
+license: apache-2.0
+datasets:
+- go_emotions
+model-index:
+- name: fine_tuned_bert_emotions_large
+  results:
+  - task:
+      name: Multi-label Emotion Classification
+      type: text-classification
+    dataset:
+      name: GoEmotions
+      type: go_emotions
+      split: test
+    metrics:
+    - name: F1 (micro)
+      type: f1
+      value: 0.53
+    - name: F1 (macro)
+      type: f1
+      value: 0.41
+    - name: Accuracy
+      type: accuracy
+      value: 0.38
+base_model:
+- google-bert/bert-large-uncased
+---
+
+# fine_tuned_bert_emotions_large
+
+## Model summary
+- Base: `bert-large-uncased`
+- Task: multi-label emotion classification (GoEmotions-level emotions)
+- Fine-tuning: tri-tower setup with contrastive context/label alignment
+- Max length: 256
+- Labels: same 28 GoEmotions emotions (excluding `example_very_unclear`)
+
+## Intended use
+- Classify short texts (social posts, chats) with multiple emotions.
+- Not for medical/mental-health diagnosis; avoid high-stakes use without human review.
+
+## Training data
+- GoEmotions dataset
+- Preprocessing: standard HF tokenizer, lowercased, truncation at 256 tokens.
+
+## Training procedure
+- Optimizer: AdamW, LR 5e-5 (context head 2e-5), cosine scheduler, warmup 10%.
+- Batch size: 8 (eval 32), epochs: 40 (early stop on val_f1_micro).
+- Losses: BCE-with-logits for context, InfoNCE contrastive temperature 0.07, context loss weight 1.0.
+- Regularization: dropout 0.1–0.2 (head), label smoothing 0.05.
+- Hardware: NVIDIA GPU (NVIDIA GeForce RTX 5090 (sm_120)).
+
+## Evaluation
+Replace with your best numbers:
+- Test F1 (micro): 0.53
+- Test F1 (macro): 0.41
+- Precision (micro): 0.47
+- Accuracy: 0.38
+- Thresholding: per-label tuned on validation split.
+
+## How to use
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "your-hf-username/fine_tuned_bert_emotions_large"
+tok = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "I’m excited but a bit nervous about tomorrow!"
+enc = tok(text, return_tensors="pt", truncation=True, padding=True)
+with torch.no_grad():
+    logits = model(**enc).logits
+probs = torch.sigmoid(logits)[0]
+label_map = model.config.id2label
+preds = [(label_map[i], probs[i].item()) for i in range(len(probs))]
+print(sorted(preds, key=lambda x: x[1], reverse=True)[:5])